Minimum hypervolume clustering algorithm

Authors:
Jacek Leski;Norbert Henzel
Affiliations:
Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice. Poland;Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice. Poland
Venue:
Machine Graphics & Vision International Journal
Year:
2002

Citing 3
Cited 0

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Abstract

The Hard C-Means (HCM) clustering method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantage of this method is that the performance of the HCM is good only when the data set contains clusters that have approximately the same size and shape. The paper is devoted to a new clustering algorithm, called minimum hypervolume clustering (MHC), that seeks c hyperellipsoids with the smallest hypervolumes that enclose all the data points. Performances of the new clustering algorithm are experimentally verified using synthetic and real life data containing clusters with different sizes and orientations.